Amazon Personalize Runtime

Name: Amazon Personalize Runtime API
Brand: Amazon Personalize Runtime
Availability: InStock

★ Only Publicly Available OpenAPI DocumentAI/MLMl Inferencehmac2 EndpointsREST

For Agents

Fetch personalized item recommendations and rerank candidate lists for a specific user from a trained Amazon Personalize model.

Connect the Amazon Personalize Runtime to your agent

Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon Personalize Runtime, or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.

Install Jentic One Beta (Self-Hosted)

Jentic One Host machine

# On the machine that will host your Jentic One instance:
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh

Agent machine

# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL https://raw.githubusercontent.com/jentic/jentic-one/main/tools/install.sh | sh
jentic register       # connects your agent to your Jentic One instance

Jentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.

Capabilities

What an agent can do with Amazon Personalize Runtime API.

Return ranked item recommendations for a userId from a deployed Personalize campaign or recommender

Rerank a candidate inputList of itemIds against a personalization campaign for a given user

Pass real-time context features (deviceType, location, weather) that influence recommendation scoring

Filter recommendations using a Personalize filter ARN with dynamic filterValues such as exclude-purchased

GET STARTED

Start building with Amazon Personalize Runtime API

Explore with Jentic

View OpenAPI Document

Use for: I need to fetch personalized product recommendations for a logged-in shopper, Rerank a candidate list of article IDs for a specific reader, Get the top 25 recommended videos for user 1234 from my Personalize campaign, Retrieve recommendations and exclude items the user has already purchased

Not supported: Does not train models, manage datasets, or create campaigns — use for real-time inference against an already-deployed Amazon Personalize campaign or recommender only.

Jentic publishes the only available OpenAPI document for Amazon Personalize Runtime, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon Personalize Runtime, keeping it validated and agent-ready. Amazon Personalize Runtime serves real-time personalized recommendations and re-ranked item lists from machine learning models trained in Amazon Personalize. The service exposes two inference endpoints that consume a deployed campaign or recommender ARN and return ranked itemIds tailored to a specific user, context, or input list. It is designed for live serving paths inside e-commerce, media, and content discovery applications where models built in Personalize need to be queried at request time.

Use Cases

Patterns agents use Amazon Personalize Runtime API for, with concrete tasks.

★ Real-Time E-Commerce Recommendations

Power product carousels and 'recommended for you' modules on storefront pages by calling a deployed Personalize campaign at request time. The Runtime API takes a userId and campaignArn and returns a ranked list of itemIds with optional metadata columns, so the storefront can render personalized slates in tens of milliseconds. Use filters to exclude out-of-stock or already-purchased items without retraining the model.

Call GetRecommendations with campaignArn for the homepage campaign, userId 'shopper-7842', numResults 12, and filterArn excluding purchased items, then return the ranked itemIds

Search Result Reranking

Take an existing list of candidate items from a search engine or content index and rerank it per user using a Personalized-Ranking recipe. The PersonalizeRanking endpoint accepts the inputList plus userId and returns the same items reordered by predicted relevance, with optional context metadata. This lets teams keep their primary search infrastructure and layer personalization on top.

Call GetPersonalizedRanking with the campaignArn for the personalized-ranking campaign, userId 'user-991', and an inputList of 50 candidate articleIds, and return the reranked order

Contextual Content Discovery

Serve different recommendations based on real-time signals like deviceType, location, or time of day by passing a context map into each request. Amazon Personalize Runtime applies the context to the trained model so a mobile evening session and a desktop weekday session can produce distinct slates from the same campaign. Useful for media catalogues, news apps, and travel discovery.

Call GetRecommendations for userId 'reader-44' with context {'DEVICE': 'mobile', 'TIME_OF_DAY': 'evening'} and numResults 20

AI Agent Recommendation Tool via Jentic

An AI shopping or content agent uses Jentic to discover the Personalize Runtime operation, load its input schema, and call it with a userId pulled from session state. The agent never holds the AWS credentials directly: Jentic injects SigV4-signed requests using the configured AWS access key from its vault. The agent receives a ranked itemIds list and can pass the result downstream to a product-detail tool or a response composer.

Use Jentic to search 'get personalized recommendations for a user', load GetRecommendations, and execute it with the user's session id and the homepage campaignArn

Key Endpoints

2 endpoints — jentic publishes the only available openapi specification for amazon personalize runtime, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/recommendations

Get a ranked list of recommended itemIds for a user from a campaign or recommender

POST

/personalize-ranking

Rerank an inputList of itemIds for a specific user against a personalized-ranking campaign

POST

/recommendations

Get a ranked list of recommended itemIds for a user from a campaign or recommender

POST

/personalize-ranking

Rerank an inputList of itemIds for a specific user against a personalized-ranking campaign

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

AWS access keys for Personalize Runtime are stored encrypted in the Jentic vault. Jentic generates SigV4 signatures per request, so agents never receive raw AWS secret keys.

Intent-based discovery

Agents search by intent (e.g., 'get personalized recommendations for a user') and Jentic returns the matching Personalize Runtime operation with its input schema, so the agent calls the right endpoint without browsing AWS docs.

Time to first call

Direct integration: 1-2 days for AWS SDK setup, SigV4 signing, IAM policy scoping, and error handling. Through Jentic: under 30 minutes — search, load schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Amazon SageMaker

Build, train and host custom recommendation or ranking models when Personalize's recipes are not flexible enough

Choose SageMaker when the team wants full control over the model architecture, custom features, or non-recommendation ML tasks. Use Personalize Runtime when a managed recommendation recipe is sufficient.

Complementary

Amazon Kendra

Pair enterprise semantic search with personalized reranking of result sets

Use Kendra to retrieve a candidate document set, then rerank with Personalize Runtime's PersonalizedRanking for per-user ordering.

Complementary

Amazon Comprehend

Generate text features and topic signals to enrich items before serving recommendations

Use Comprehend to extract entities or topics from item text, store them as item metadata, then call Personalize Runtime to surface recommendations over those enriched items.

FAQs

Specific to using Amazon Personalize Runtime API through Jentic.

Why is there no official OpenAPI spec for Amazon Personalize Runtime?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Personalize Runtime via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Amazon Personalize Runtime API use?

The API uses AWS SigV4 HMAC request signing with an AWS access key ID and secret access key. Through Jentic, AWS credentials are stored in the Jentic vault and SigV4 signatures are generated per call, so the agent never sees raw secret keys.

Can I rerank an existing list of items with this API?

Yes. Call POST /personalize-ranking with a campaignArn pointing to a campaign trained on a personalized-ranking recipe, a userId, and an inputList of itemIds. The response returns the same items reordered by predicted relevance for that user.

What are the rate limits for Amazon Personalize Runtime?

Personalize Runtime applies per-region transactions-per-second quotas at the campaign level rather than fixed API rate limits. The default minProvisionedTPS on a campaign is 1; raise it on the campaign to support higher request rates. Throttled calls return a ProvisionedThroughputExceededException.

How do I get personalized recommendations for a user through Jentic?

Search Jentic for 'get personalized recommendations for a user', load the GetRecommendations operation, and execute it with your campaignArn and userId. The Jentic SDK handles SigV4 signing using credentials stored in the vault. Install with pip install jentic.

Is Amazon Personalize Runtime free?

No. Personalize Runtime charges per recommendation request beyond the free tier and you also pay for the underlying campaign or recommender's provisioned TPS. See the AWS Personalize pricing page for the current per-request and per-TPS-hour rates.